COVID-19 in India: What Went So Terribly Wrong?

Motivation

Recently, India has experienced an increase in COVID-19 cases and deaths. With insufficient medical resources and poor living conditions, many people have to unfortunately be turned away from hospitals in favor of people with more severe cases. But until now, it seemed as if India was doing well with the pandemic! So what has happened recently to change that? In this notebook we will explore that question and investigate which locations within India have it the worst and need medical resources the most.

Data Source

https://github.com/datameet/covid19

This data combines multiple data sources from Indian government websites into a cleaner and more accesible format. The government sources for COVID-19 data are the Ministry of Health & Family Welfare and The Indian Council of Medical Research, or ICMR. The data is stored in multiple files representing different pieces of data, such as the total number of cases as a time series, and the number of cases and deaths per state. Each piece of data is stored as a .json file and will need to be preprocessed so that we can work with it more easily.

We will start by importing some of the libraries we will need. The requests library is used to acquire the .json file from the internet and get its contents in a JSON format. The json library is then used to place that data into a local json file and convert it into a dictionary. Finally, we use pandas to convert the dictionary into a dataframe so that we can more easily plot and visualize the data.

import requests
import json
import pandas as pd

Because the data is stored in a specific JSON format, we need to read the JSON file from the web and read it into a JSON data structure. We will start by acquiring the all_totals.json file, which contains the totals for the number of active cases, number of deaths, number of people cured, and the total number of confirmed cases, all with the associated timestamps.

# Takes a JSON file from the data source and places its contents inside a dictionary
def get_json(json_file):
    link = 'https://raw.githubusercontent.com/datameet/covid19/master/data/' + json_file
    r = requests.get(link)
    data = r.json()

    # takes the data from link and saves it in a JSON file
    with open(json_file, 'w') as f:
        json.dump(data, f)

    # takes the JSON file and places the contents in a dictionary
    with open(json_file) as f:
        data = json.load(f)

    return data

data = get_json('all_totals.json')
data

', 'death'], 'value': 20160},
  {'key': ['2020-07-07T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 719665},
  {'key': ['2020-07-08T08:00:00.00+05:30', 'active_cases'], 'value': 264944},
  {'key': ['2020-07-08T08:00:00.00+05:30', 'cured'], 'value': 456831},
  {'key': ['2020-07-08T08:00:00.00+05:30', 'death'], 'value': 20642},
  {'key': ['2020-07-08T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 742417},
  {'key': ['2020-07-09T08:00:00.00+05:30', 'active_cases'], 'value': 269789},
  {'key': ['2020-07-09T08:00:00.00+05:30', 'cured'], 'value': 476378},
  {'key': ['2020-07-09T08:00:00.00+05:30', 'death'], 'value': 21129},
  {'key': ['2020-07-09T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 767296},
  {'key': ['2020-07-10T08:00:00.00+05:30', 'active_cases'], 'value': 276685},
  {'key': ['2020-07-10T08:00:00.00+05:30', 'cured'], 'value': 495513},
  {'key': ['2020-07-10T08:00:00.00+05:30', 'death'], 'value': 21604},
  {'key': ['2020-07-10T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 793802},
  {'key': ['2020-07-11T08:00:00.00+05:30', 'active_cases'], 'value': 283407},
  {'key': ['2020-07-11T08:00:00.00+05:30', 'cured'], 'value': 515386},
  {'key': ['2020-07-11T08:00:00.00+05:30', 'death'], 'value': 22123},
  {'key': ['2020-07-11T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 820916},
  {'key': ['2020-07-12T08:00:00.00+05:30', 'active_cases'], 'value': 292258},
  {'key': ['2020-07-12T08:00:00.00+05:30', 'cured'], 'value': 534621},
  {'key': ['2020-07-12T08:00:00.00+05:30', 'death'], 'value': 22674},
  {'key': ['2020-07-12T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 849553},
  {'key': ['2020-07-13T08:00:00.00+05:30', 'active_cases'], 'value': 301609},
  {'key': ['2020-07-13T08:00:00.00+05:30', 'cured'], 'value': 553471},
  {'key': ['2020-07-13T08:00:00.00+05:30', 'death'], 'value': 23174},
  {'key': ['2020-07-13T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 878254},
  {'key': ['2020-07-14T08:00:00.00+05:30', 'active_cases'], 'value': 311565},
  {'key': ['2020-07-14T08:00:00.00+05:30', 'cured'], 'value': 571460},
  {'key': ['2020-07-14T08:00:00.00+05:30', 'death'], 'value': 23727},
  {'key': ['2020-07-14T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 906752},
  {'key': ['2020-07-15T08:00:00.00+05:30', 'active_cases'], 'value': 319840},
  {'key': ['2020-07-15T08:00:00.00+05:30', 'cured'], 'value': 592032},
  {'key': ['2020-07-15T08:00:00.00+05:30', 'death'], 'value': 24309},
  {'key': ['2020-07-15T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 936181},
  {'key': ['2020-07-16T08:00:00.00+05:30', 'active_cases'], 'value': 331146},
  {'key': ['2020-07-16T08:00:00.00+05:30', 'cured'], 'value': 612815},
  {'key': ['2020-07-16T08:00:00.00+05:30', 'death'], 'value': 24915},
  {'key': ['2020-07-16T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 968876},
  {'key': ['2020-07-17T08:00:00.00+05:30', 'active_cases'], 'value': 342473},
  {'key': ['2020-07-17T08:00:00.00+05:30', 'cured'], 'value': 635757},
  {'key': ['2020-07-17T08:00:00.00+05:30', 'death'], 'value': 25602},
  {'key': ['2020-07-17T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1003832},
  {'key': ['2020-07-18T08:00:00.00+05:30', 'active_cases'], 'value': 358692},
  {'key': ['2020-07-18T08:00:00.00+05:30', 'cured'], 'value': 653751},
  {'key': ['2020-07-18T08:00:00.00+05:30', 'death'], 'value': 26273},
  {'key': ['2020-07-18T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1038716},
  {'key': ['2020-07-19T08:00:00.00+05:30', 'active_cases'], 'value': 373379},
  {'key': ['2020-07-19T08:00:00.00+05:30', 'cured'], 'value': 677423},
  {'key': ['2020-07-19T08:00:00.00+05:30', 'death'], 'value': 26816},
  {'key': ['2020-07-19T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1077618},
  {'key': ['2020-07-20T08:00:00.00+05:30', 'active_cases'], 'value': 390459},
  {'key': ['2020-07-20T08:00:00.00+05:30', 'cured'], 'value': 700087},
  {'key': ['2020-07-20T08:00:00.00+05:30', 'death'], 'value': 27497},
  {'key': ['2020-07-20T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1118043},
  {'key': ['2020-07-21T08:00:00.00+05:30', 'active_cases'], 'value': 402529},
  {'key': ['2020-07-21T08:00:00.00+05:30', 'cured'], 'value': 724578},
  {'key': ['2020-07-21T08:00:00.00+05:30', 'death'], 'value': 28084},
  {'key': ['2020-07-21T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1155191},
  {'key': ['2020-07-22T08:00:00.00+05:30', 'active_cases'], 'value': 411133},
  {'key': ['2020-07-22T08:00:00.00+05:30', 'cured'], 'value': 753050},
  {'key': ['2020-07-22T08:00:00.00+05:30', 'death'], 'value': 28732},
  {'key': ['2020-07-22T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1192915},
  {'key': ['2020-07-23T08:00:00.00+05:30', 'active_cases'], 'value': 426167},
  {'key': ['2020-07-23T08:00:00.00+05:30', 'cured'], 'value': 782607},
  {'key': ['2020-07-23T08:00:00.00+05:30', 'death'], 'value': 29861},
  {'key': ['2020-07-23T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1238635},
  {'key': ['2020-07-24T08:00:00.00+05:30', 'active_cases'], 'value': 440135},
  {'key': ['2020-07-24T08:00:00.00+05:30', 'cured'], 'value': 817209},
  {'key': ['2020-07-24T08:00:00.00+05:30', 'death'], 'value': 30601},
  {'key': ['2020-07-24T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1287945},
  {'key': ['2020-07-25T08:00:00.00+05:30', 'active_cases'], 'value': 456071},
  {'key': ['2020-07-25T08:00:00.00+05:30', 'cured'], 'value': 849432},
  {'key': ['2020-07-25T08:00:00.00+05:30', 'death'], 'value': 31358},
  {'key': ['2020-07-25T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1336861},
  {'key': ['2020-07-26T08:00:00.00+05:30', 'active_cases'], 'value': 467882},
  {'key': ['2020-07-26T08:00:00.00+05:30', 'cured'], 'value': 885577},
  {'key': ['2020-07-26T08:00:00.00+05:30', 'death'], 'value': 32063},
  {'key': ['2020-07-26T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1385522},
  {'key': ['2020-07-27T08:00:00.00+05:30', 'active_cases'], 'value': 485114},
  {'key': ['2020-07-27T08:00:00.00+05:30', 'cured'], 'value': 917568},
  {'key': ['2020-07-27T08:00:00.00+05:30', 'death'], 'value': 32771},
  {'key': ['2020-07-27T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1435453},
  {'key': ['2020-07-28T08:00:00.00+05:30', 'active_cases'], 'value': 496988},
  {'key': ['2020-07-28T08:00:00.00+05:30', 'cured'], 'value': 952743},
  {'key': ['2020-07-28T08:00:00.00+05:30', 'death'], 'value': 33425},
  {'key': ['2020-07-28T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 1483156},
  {'key': ['2020-07-29T08:00:00.00+05:30', 'active_cases'], 'value': 509447},
  {'key': ['2020-07-29T08:00:00.00+05:30', 'cured'], 'value': 988029},
  {'key': ['2020-07-29T08:00:00.00+05:30', 'death'], 'value': 34193},
  {'key': ['2020-07-29T08:00:00.00+05:30', 'total_confirmed_cases'],
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  {'key': ['2020-07-30T08:00:00.00+05:30', 'active_cases'], 'value': 528242},
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  {'key': ['2020-07-30T08:00:00.00+05:30', 'total_confirmed_cases'],
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  {'key': ['2020-07-31T08:00:00.00+05:30', 'active_cases'], 'value': 545318},
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  {'key': ['2020-08-27T08:00:00.00+05:30', 'active_cases'], 'value': 725991},
  {'key': ['2020-08-27T08:00:00.00+05:30', 'cured'], 'value': 2523771},
  {'key': ['2020-08-27T08:00:00.00+05:30', 'death'], 'value': 60472},
  {'key': ['2020-08-27T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 3310234},
  {'key': ['2020-08-28T08:00:00.00+05:30', 'active_cases'], 'value': 742023},
  {'key': ['2020-08-28T08:00:00.00+05:30', 'cured'], 'value': 2583948},
  {'key': ['2020-08-28T08:00:00.00+05:30', 'death'], 'value': 61529},
  {'key': ['2020-08-28T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 3387500},
  {'key': ['2020-08-29T08:00:00.00+05:30', 'active_cases'], 'value': 752424},
  {'key': ['2020-08-29T08:00:00.00+05:30', 'cured'], 'value': 2648998},
  {'key': ['2020-08-29T08:00:00.00+05:30', 'death'], 'value': 62550},
  {'key': ['2020-08-29T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 3463972},
  {'key': ['2020-08-30T08:00:00.00+05:30', 'active_cases'], 'value': 765302},
  {'key': ['2020-08-30T08:00:00.00+05:30', 'cured'], 'value': 2713933},
  {'key': ['2020-08-30T08:00:00.00+05:30', 'death'], 'value': 63498},
  {'key': ['2020-08-30T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 3542733},
  {'key': ['2020-08-31T08:00:00.00+05:30', 'active_cases'], 'value': 781975},
  {'key': ['2020-08-31T08:00:00.00+05:30', 'cured'], 'value': 2774801},
  {'key': ['2020-08-31T08:00:00.00+05:30', 'death'], 'value': 64469},
  {'key': ['2020-08-31T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 3621245},
  {'key': ['2020-09-01T08:00:00.00+05:30', 'active_cases'], 'value': 785996},
  {'key': ['2020-09-01T08:00:00.00+05:30', 'cured'], 'value': 2839882},
  {'key': ['2020-09-01T08:00:00.00+05:30', 'death'], 'value': 65288},
  {'key': ['2020-09-01T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 3691166},
  {'key': ['2020-09-02T08:00:00.00+05:30', 'active_cases'], 'value': 801282},
  {'key': ['2020-09-02T08:00:00.00+05:30', 'cured'], 'value': 2901908},
  {'key': ['2020-09-02T08:00:00.00+05:30', 'death'], 'value': 66333},
  {'key': ['2020-09-02T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 3769523},
  {'key': ['2020-09-03T08:00:00.00+05:30', 'active_cases'], 'value': 815538},
  {'key': ['2020-09-03T08:00:00.00+05:30', 'cured'], 'value': 2970492},
  {'key': ['2020-09-03T08:00:00.00+05:30', 'death'], 'value': 67376},
  {'key': ['2020-09-03T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 3853406},
  {'key': ['2020-09-04T08:00:00.00+05:30', 'active_cases'], 'value': 831124},
  {'key': ['2020-09-04T08:00:00.00+05:30', 'cured'], 'value': 3037151},
  {'key': ['2020-09-04T08:00:00.00+05:30', 'death'], 'value': 68472},
  {'key': ['2020-09-04T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 3936747},
  {'key': ['2020-09-05T08:00:00.00+05:30', 'active_cases'], 'value': 846395},
  {'key': ['2020-09-05T08:00:00.00+05:30', 'cured'], 'value': 3107223},
  {'key': ['2020-09-05T08:00:00.00+05:30', 'death'], 'value': 69561},
  {'key': ['2020-09-05T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 4023179},
  {'key': ['2020-09-06T08:00:00.00+05:30', 'active_cases'], 'value': 862320},
  {'key': ['2020-09-06T08:00:00.00+05:30', 'cured'], 'value': 3180865},
  {'key': ['2020-09-06T08:00:00.00+05:30', 'death'], 'value': 70626},
  {'key': ['2020-09-06T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 4113811},
  {'key': ['2020-09-07T08:00:00.00+05:30', 'active_cases'], 'value': 882542},
  {'key': ['2020-09-07T08:00:00.00+05:30', 'cured'], 'value': 3250429},
  {'key': ['2020-09-07T08:00:00.00+05:30', 'death'], 'value': 71642},
  {'key': ['2020-09-07T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 4204613},
  {'key': ['2020-09-08T08:00:00.00+05:30', 'active_cases'], 'value': 883697},
  {'key': ['2020-09-08T08:00:00.00+05:30', 'cured'], 'value': 3323950},
  {'key': ['2020-09-08T08:00:00.00+05:30', 'death'], 'value': 72775},
  {'key': ['2020-09-08T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 4280422},
  {'key': ['2020-09-09T08:00:00.00+05:30', 'active_cases'], 'value': 897394},
  {'key': ['2020-09-09T08:00:00.00+05:30', 'cured'], 'value': 3398844},
  {'key': ['2020-09-09T08:00:00.00+05:30', 'death'], 'value': 73890},
  {'key': ['2020-09-09T08:00:00.00+05:30', 'total_confirmed_cases'],
   'value': 4370128},
  ...]}

As you can see, the data is stored in a key-value pair format, where the key contains the timestamp and attribute name, while the value contains the number associated with that attribute. We can wrangle this data format into a more table-like structure so that we can convert this dictionary into a dataframe.

# Takes input from the JSON dictionary and converts it into
# a new dictionary with a different format. The attributes
# found in the input ("cases", "deaths", etc.) are keys in the
# new dictionary, and the values are a single key-value pair
# of time and the value corresponding to the attribute.
def extract_data(data):
    extracted_data = {}
    rows = data["rows"]

    for row in rows:
        time, attribute, value = extract_row(row)

        if attribute not in extracted_data:
            extracted_data[attribute] = {time : value}
        else:
            extracted_data[attribute][time] = value

    return extracted_data

# Extracts the time, attribute, and value information
# from one row of the JSON dictionary. Used as a helper
# function in extract_data()
def extract_row(row):
    time = row['key'][0]
    attribute = row['key'][1]
    value = row['value']

    return time[:10], attribute, value

data_dict = extract_data(data)
df = pd.DataFrame(data_dict)
df

	active_cases	cured	death	total_confirmed_cases
2020-01-30	1	0	0	1
2020-02-02	2	0	0	2
2020-02-03	3	0	0	3
2020-03-02	5	0	0	5
2020-03-03	6	0	0	6
...	...	...	...	...
2021-05-09	3736648	18317404	242362	22296414
2021-05-10	3745237	18671222	246116	22662575
2021-05-11	3715221	19027304	249992	22992517
2021-05-12	3704099	19382642	254197	23340938
2021-05-13	3710525	19734823	258317	23703665

435 rows × 4 columns

There is a problem here with the date ranges, and that is that they are not continuous! If we ever want to visualize the data, it will be important to have a continuous date range. We can do this by adding new rows for the missing days, and simply taking the previous row's values as the values for the new rows. We fill in the missing values in this way because all of the metrics in the dataset are cumulative.

# calculate date range index using lowest and highest dates
idx = pd.date_range(df.index.min(), df.index.max())
df.index = pd.DatetimeIndex(df.index)

# reindex the data using date range, and fill any missing dates
# with the previous date's row values
df = df.reindex(index=idx, method='ffill')

df

	active_cases	cured	death	total_confirmed_cases
2020-01-30	1	0	0	1
2020-01-31	1	0	0	1
2020-02-01	1	0	0	1
2020-02-02	2	0	0	2
2020-02-03	3	0	0	3
...	...	...	...	...
2021-05-09	3736648	18317404	242362	22296414
2021-05-10	3745237	18671222	246116	22662575
2021-05-11	3715221	19027304	249992	22992517
2021-05-12	3704099	19382642	254197	23340938
2021-05-13	3710525	19734823	258317	23703665

470 rows × 4 columns

Now that we have the data in a managable format, we can start visualizing the data. Let's start by plotting the number of active cases, total number of confirmed cases, number of deaths, and number of people cured.

df.plot(y='active_cases')

<AxesSubplot:>

df.plot(y='total_confirmed_cases')

<AxesSubplot:>

df.plot(y='death')

<AxesSubplot:>

df.plot(y='cured')

<AxesSubplot:>

We can very clearly see from these plots that cases and deaths have been skyrocketing starting around late March to early April 2021. This is when a new strain of the virus came to India. But what made this new strain so difficult to handle compared to any previous ones, and is the situation different by state in India?

data2 = get_json('mohfw.json')
data2

med': 83,
    'death': 3}},
  {'id': '2020-03-31T20:30:00.00+05:30|kl',
   'key': '2020-03-31T20:30:00.00+05:30|kl',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|kl',
    '_rev': '1-738c6bfe4b3ccc8209aff155455f7246',
    'state': 'kl',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 19,
    'confirmed': 234,
    'death': 1}},
  {'id': '2020-03-31T20:30:00.00+05:30|la',
   'key': '2020-03-31T20:30:00.00+05:30|la',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|la',
    '_rev': '1-5d4c778dfb1cae0950f075924766c058',
    'state': 'la',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 3,
    'confirmed': 13,
    'death': 0}},
  {'id': '2020-03-31T20:30:00.00+05:30|mh',
   'key': '2020-03-31T20:30:00.00+05:30|mh',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|mh',
    '_rev': '1-84e5ecdef08bb45e7ac52269bf4fb314',
    'state': 'mh',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 39,
    'confirmed': 216,
    'death': 9}},
  {'id': '2020-03-31T20:30:00.00+05:30|mn',
   'key': '2020-03-31T20:30:00.00+05:30|mn',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|mn',
    '_rev': '1-3b5dce76f3b5291f9ea00493a86e4ecc',
    'state': 'mn',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 0,
    'confirmed': 1,
    'death': 0}},
  {'id': '2020-03-31T20:30:00.00+05:30|mp',
   'key': '2020-03-31T20:30:00.00+05:30|mp',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|mp',
    '_rev': '1-97090e559be4db777dc4211769ca9243',
    'state': 'mp',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 0,
    'confirmed': 47,
    'death': 3}},
  {'id': '2020-03-31T20:30:00.00+05:30|mz',
   'key': '2020-03-31T20:30:00.00+05:30|mz',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|mz',
    '_rev': '1-4310de10742e9252e08318f47391158f',
    'state': 'mz',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 0,
    'confirmed': 1,
    'death': 0}},
  {'id': '2020-03-31T20:30:00.00+05:30|or',
   'key': '2020-03-31T20:30:00.00+05:30|or',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|or',
    '_rev': '1-e75ec5c62f10e293d4bb6a80817ab2eb',
    'state': 'or',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 0,
    'confirmed': 3,
    'death': 0}},
  {'id': '2020-03-31T20:30:00.00+05:30|pb',
   'key': '2020-03-31T20:30:00.00+05:30|pb',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|pb',
    '_rev': '1-c69087dff64a71adec17bb61d74e845a',
    'state': 'pb',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 1,
    'confirmed': 41,
    'death': 3}},
  {'id': '2020-03-31T20:30:00.00+05:30|py',
   'key': '2020-03-31T20:30:00.00+05:30|py',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|py',
    '_rev': '1-5d7dbab67042c3234890ab6bd15f5d92',
    'state': 'py',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 0,
    'confirmed': 1,
    'death': 0}},
  {'id': '2020-03-31T20:30:00.00+05:30|rj',
   'key': '2020-03-31T20:30:00.00+05:30|rj',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|rj',
    '_rev': '1-b6f2f51990c53dc7c94fd9a2d282d22c',
    'state': 'rj',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 3,
    'confirmed': 74,
    'death': 0}},
  {'id': '2020-03-31T20:30:00.00+05:30|tg',
   'key': '2020-03-31T20:30:00.00+05:30|tg',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|tg',
    '_rev': '1-e01b13cefed57a525bd763a0d41874f8',
    'state': 'tg',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 1,
    'confirmed': 79,
    'death': 1}},
  {'id': '2020-03-31T20:30:00.00+05:30|tn',
   'key': '2020-03-31T20:30:00.00+05:30|tn',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|tn',
    '_rev': '1-bd63b887b78543bc53779a054b92c03f',
    'state': 'tn',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 4,
    'confirmed': 74,
    'death': 1}},
  {'id': '2020-03-31T20:30:00.00+05:30|up',
   'key': '2020-03-31T20:30:00.00+05:30|up',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|up',
    '_rev': '1-5043e1e7d2f4c63d47b048cd51c68007',
    'state': 'up',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 14,
    'confirmed': 101,
    'death': 0}},
  {'id': '2020-03-31T20:30:00.00+05:30|ut',
   'key': '2020-03-31T20:30:00.00+05:30|ut',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|ut',
    '_rev': '1-fdd1cfc9c6c344a1e08528b6e6c24c9e',
    'state': 'ut',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 2,
    'confirmed': 7,
    'death': 0}},
  {'id': '2020-03-31T20:30:00.00+05:30|wb',
   'key': '2020-03-31T20:30:00.00+05:30|wb',
   'value': {'_id': '2020-03-31T20:30:00.00+05:30|wb',
    '_rev': '1-7237404564471b48490df1671211ce61',
    'state': 'wb',
    'type': 'cases',
    'report_time': '2020-03-31T20:30:00.00+05:30',
    'source': 'mohfw',
    'cured': 0,
    'confirmed': 26,
    'death': 2}},
  {'id': '2020-04-01T09:00:00.00+05:30|an',
   'key': '2020-04-01T09:00:00.00+05:30|an',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|an',
    '_rev': '1-2dea80a1f3a4f88c534bb0b81c041a9f',
    'state': 'an',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 10,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|ap',
   'key': '2020-04-01T09:00:00.00+05:30|ap',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|ap',
    '_rev': '1-a2523deee84ed579fcd4accc01cddc60',
    'state': 'ap',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 83,
    'cured': 1,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|br',
   'key': '2020-04-01T09:00:00.00+05:30|br',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|br',
    '_rev': '1-389121d97b1037ebe1965b8e273a1d94',
    'state': 'br',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 23,
    'cured': 0,
    'death': 1,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|ch',
   'key': '2020-04-01T09:00:00.00+05:30|ch',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|ch',
    '_rev': '1-cc57dac9b601782638bd5d580c9130c7',
    'state': 'ch',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 13,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|ct',
   'key': '2020-04-01T09:00:00.00+05:30|ct',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|ct',
    '_rev': '1-cbb06cf9ea67ae1e15c51925cf64a121',
    'state': 'ct',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 9,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|dl',
   'key': '2020-04-01T09:00:00.00+05:30|dl',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|dl',
    '_rev': '1-ddeb0178f100e20ccc0ffbf91fc46300',
    'state': 'dl',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 120,
    'cured': 6,
    'death': 2,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|ga',
   'key': '2020-04-01T09:00:00.00+05:30|ga',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|ga',
    '_rev': '1-5e5948536ac7990fa7ddf6718af00590',
    'state': 'ga',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 5,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|gj',
   'key': '2020-04-01T09:00:00.00+05:30|gj',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|gj',
    '_rev': '1-7eb118d020f1bf214774c8715b40e8c5',
    'state': 'gj',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 74,
    'cured': 5,
    'death': 6,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|hp',
   'key': '2020-04-01T09:00:00.00+05:30|hp',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|hp',
    '_rev': '1-8a269427a7b73ac2d356eee8d9137159',
    'state': 'hp',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 3,
    'cured': 0,
    'death': 1,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|hr',
   'key': '2020-04-01T09:00:00.00+05:30|hr',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|hr',
    '_rev': '1-ab730d43312b42fca5e970c93a18102a',
    'state': 'hr',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 43,
    'cured': 21,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|jk',
   'key': '2020-04-01T09:00:00.00+05:30|jk',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|jk',
    '_rev': '1-79a6c136cb7100b0d0a9ff0fba8fe6e0',
    'state': 'jk',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 55,
    'cured': 2,
    'death': 2,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|ka',
   'key': '2020-04-01T09:00:00.00+05:30|ka',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|ka',
    '_rev': '1-d561828b485767e9269c3cc5906e768e',
    'state': 'ka',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 101,
    'cured': 8,
    'death': 3,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|kl',
   'key': '2020-04-01T09:00:00.00+05:30|kl',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|kl',
    '_rev': '1-82c1907ab22dc77a7f959d339e7b41b5',
    'state': 'kl',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 241,
    'cured': 23,
    'death': 2,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|la',
   'key': '2020-04-01T09:00:00.00+05:30|la',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|la',
    '_rev': '1-ee19cce132d4c39c7caa35d3ec28534e',
    'state': 'la',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 13,
    'cured': 3,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|mh',
   'key': '2020-04-01T09:00:00.00+05:30|mh',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|mh',
    '_rev': '1-382abb0d6b69fb84a3803ef656ea6633',
    'state': 'mh',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 302,
    'cured': 39,
    'death': 9,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|mn',
   'key': '2020-04-01T09:00:00.00+05:30|mn',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|mn',
    '_rev': '1-8b57e9d96f61449dea574ebe10b37d02',
    'state': 'mn',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 1,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|mp',
   'key': '2020-04-01T09:00:00.00+05:30|mp',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|mp',
    '_rev': '1-b7b10a85510a2439c6d47f7e7765fa7a',
    'state': 'mp',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 47,
    'cured': 0,
    'death': 3,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|mz',
   'key': '2020-04-01T09:00:00.00+05:30|mz',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|mz',
    '_rev': '1-90823eed5c73b5f6a9347c73f977daa9',
    'state': 'mz',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 1,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|or',
   'key': '2020-04-01T09:00:00.00+05:30|or',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|or',
    '_rev': '1-a48f7ef5aa70d851b08614dc3f74524d',
    'state': 'or',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 4,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|pb',
   'key': '2020-04-01T09:00:00.00+05:30|pb',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|pb',
    '_rev': '1-0c657a0f0da1cee94b920adfbf2df84a',
    'state': 'pb',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 41,
    'cured': 1,
    'death': 3,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|py',
   'key': '2020-04-01T09:00:00.00+05:30|py',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|py',
    '_rev': '1-bde26436481e0bad9c4dc99ca1aafe6a',
    'state': 'py',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 1,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|rj',
   'key': '2020-04-01T09:00:00.00+05:30|rj',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|rj',
    '_rev': '1-ada744c0d20dcbaadd4d3d795dcd5cc2',
    'state': 'rj',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 93,
    'cured': 3,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|tg',
   'key': '2020-04-01T09:00:00.00+05:30|tg',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|tg',
    '_rev': '1-022d433ab82f68ccd6502ffcac7d2d3f',
    'state': 'tg',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 94,
    'cured': 1,
    'death': 3,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|tn',
   'key': '2020-04-01T09:00:00.00+05:30|tn',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|tn',
    '_rev': '1-ef1fce374096cc15f7982814fcbc5fe2',
    'state': 'tn',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 124,
    'cured': 4,
    'death': 1,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|up',
   'key': '2020-04-01T09:00:00.00+05:30|up',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|up',
    '_rev': '1-fe40869e34040aefa29a5d1f526463b7',
    'state': 'up',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 103,
    'cured': 14,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|ut',
   'key': '2020-04-01T09:00:00.00+05:30|ut',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|ut',
    '_rev': '1-cd6a945c8269e68efedf3fcb0f23ec7c',
    'state': 'ut',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 7,
    'cured': 2,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T09:00:00.00+05:30|wb',
   'key': '2020-04-01T09:00:00.00+05:30|wb',
   'value': {'_id': '2020-04-01T09:00:00.00+05:30|wb',
    '_rev': '1-00d108c5eb80bf944df10dfda313da20',
    'state': 'wb',
    'report_time': '2020-04-01T09:00:00.00+05:30',
    'confirmed': 26,
    'cured': 0,
    'death': 2,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|an',
   'key': '2020-04-01T19:30:00.00+05:30|an',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|an',
    '_rev': '1-2bd4e7faf192a281c11adf36efd2bedb',
    'state': 'an',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 10,
    'cured': 1,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|ap',
   'key': '2020-04-01T19:30:00.00+05:30|ap',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|ap',
    '_rev': '1-be02347603c55bcfef15649bef3002ea',
    'state': 'ap',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 83,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|as',
   'key': '2020-04-01T19:30:00.00+05:30|as',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|as',
    '_rev': '1-7d3325ff85d493b417c1bb2b15b847fe',
    'state': 'as',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 1,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|br',
   'key': '2020-04-01T19:30:00.00+05:30|br',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|br',
    '_rev': '1-3445975f9c3f7b3cef908446bd10aa58',
    'state': 'br',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 23,
    'cured': 0,
    'death': 1,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|ch',
   'key': '2020-04-01T19:30:00.00+05:30|ch',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|ch',
    '_rev': '1-c1c7ae8286b3f8481d73a053ffd44b41',
    'state': 'ch',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 16,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|ct',
   'key': '2020-04-01T19:30:00.00+05:30|ct',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|ct',
    '_rev': '1-7205d950d9a8d58c07aedd5357891064',
    'state': 'ct',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 9,
    'cured': 2,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|dl',
   'key': '2020-04-01T19:30:00.00+05:30|dl',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|dl',
    '_rev': '1-f81fbf23d30e0b80b6b98b482b2fb158',
    'state': 'dl',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 152,
    'cured': 6,
    'death': 2,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|ga',
   'key': '2020-04-01T19:30:00.00+05:30|ga',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|ga',
    '_rev': '1-7b9f6e0eed15aeb4b6e6b0ad23f5ffbb',
    'state': 'ga',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 5,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|gj',
   'key': '2020-04-01T19:30:00.00+05:30|gj',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|gj',
    '_rev': '1-f6191023e7cd85e3ff74fd5aa38fc7f2',
    'state': 'gj',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 82,
    'cured': 5,
    'death': 6,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|hp',
   'key': '2020-04-01T19:30:00.00+05:30|hp',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|hp',
    '_rev': '1-22340707ca40c15f2804cd6ff4698c8a',
    'state': 'hp',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 3,
    'cured': 0,
    'death': 1,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|hr',
   'key': '2020-04-01T19:30:00.00+05:30|hr',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|hr',
    '_rev': '1-c6c6ff99fd8af2e87d4a3410ff782386',
    'state': 'hr',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 43,
    'cured': 21,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|jh',
   'key': '2020-04-01T19:30:00.00+05:30|jh',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|jh',
    '_rev': '1-9d1525975200a47ab5704a703841c18e',
    'state': 'jh',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 1,
    'cured': 0,
    'death': 0,
    'source': 'mohfw',
    'type': 'cases'}},
  {'id': '2020-04-01T19:30:00.00+05:30|jk',
   'key': '2020-04-01T19:30:00.00+05:30|jk',
   'value': {'_id': '2020-04-01T19:30:00.00+05:30|jk',
    '_rev': '1-9ac47b472af6dc62c660f841c44230a9',
    'state': 'jk',
    'report_time': '2020-04-01T19:30:00.00+05:30',
    'confirmed': 62,
    'cured': 2,
    'death': 2,
    'source': 'mohfw',
    'type': 'cases'}},
  ...]}

data2.keys()

dict_keys(['total_rows', 'offset', 'rows'])

data2['rows'][5].keys()

dict_keys(['id', 'key', 'value'])

data2['rows'][5]['value'].keys()

dict_keys(['_id', '_rev', 'report_time', 'state', 'confirmed_india', 'confirmed_foreign', 'cured', 'death', 'source', 'type', 'confirmed'])

def add_tuples(a, b):
    return tuple(map(lambda i, j: i + j, a, b))

def get_state_from_abbrev(abbrev):
    states = {
        "ap": "Andhra Pradesh",
        "ar": "Arunachal Pradesh",
        "as": "Assam",
        "br": "Bihar",
        "ct": "Chhattisgarh",
        "ga": "Goa",
        "gj": "Gujarat",
        "hr": "Haryana",
        "hp": "Himachal Pradesh",
        "jh": "Jharkhand",
        "ka": "Karnataka",
        "kl": "Kerala",
        "mp": "Madhya Pradesh",
        "mh": "Maharashtra",
        "mn": "Manipur",
        "ml": "Meghalaya",
        "mz": "Mizoram",
        "nl": "Nagaland",
        "or": "Odisha",
        "pb": "Punjab",
        "rj": "Rajasthan",
        "sk": "Sikkim",
        "tn": "Tamil Nadu",
        "tg": "Telangana",
        "tr": "Tripura",
        "ut": "Uttarakhand",
        "up": "Uttar Pradesh",
        "wb": "West Bengal",
        "an": "Andaman and Nicobar Islands",
        "ch": "Chandigarh",
        "dn": "Dadra and Nagar Haveli",
        "dd": "Daman and Diu",
        "dl": "Delhi",
        "jk": "Jammu and Kashmir",
        "la": "Ladakh",
        "ld": "Lakshadweep",
        "py": "Pondicherry",
        "dn_dd": "Dadra and Nagar Haveli and Daman and Diu",
        "unassigned": "unassigned"
    }

    return states[abbrev]

def extract_data2(data):
    extracted_data = {}
    rows = data["rows"]

    for row in rows:
        time, state, confirmed, cured, death = extract_row2(row)

        if state not in extracted_data:
            extracted_data[state] = (confirmed, cured, death)
        else:
            res = add_tuples(extracted_data[state], (confirmed, cured, death))
            extracted_data[state] = res

    return extracted_data

# Extracts the time, attribute, and value information
# from one row of the JSON dictionary. Used as a helper
# function in extract_data()
def extract_row2(row):
    values_dict = row['value']
    time = values_dict['report_time']
    state = get_state_from_abbrev(values_dict['state'])
    confirmed = values_dict['confirmed']
    cured = values_dict['cured']
    death = values_dict['death']

    return time[:10], state, confirmed, cured, death

extracted2 = extract_data2(data2)
extracted2

{'Kerala': (200185603, 177894118, 766581),
 'Delhi': (158725093, 147896560, 2741459),
 'Telangana': (75798728, 69580382, 432535),
 'Rajasthan': (77767638, 69179429, 700476),
 'Haryana': (66067411, 60344316, 698864),
 'Jammu and Kashmir': (30756599, 27720245, 469131),
 'Karnataka': (242380060, 216792332, 3144790),
 'Ladakh': (2307394, 2084230, 28077),
 'Maharashtra': (589598167, 512544984, 13590012),
 'Punjab': (47764245, 42219368, 1406438),
 'Tamil Nadu': (223058512, 208216246, 3261644),
 'Uttar Pradesh': (160155913, 143267909, 2199456),
 'Andhra Pradesh': (228887306, 215214582, 1848944),
 'Uttarakhand': (23199910, 20397560, 370919),
 'Odisha': (82390133, 77558828, 426009),
 'West Bengal': (133436340, 123072064, 2330712),
 'Pondicherry': (9996027, 9076464, 163604),
 'Chandigarh': (5394798, 4840252, 77743),
 'Chhattisgarh': (75304232, 66298558, 875894),
 'Gujarat': (70290062, 62010591, 1330100),
 'Himachal Pradesh': (12567361, 11065632, 198464),
 'Madhya Pradesh': (66056188, 59307071, 976388),
 'Bihar': (69575592, 64292363, 381133),
 'Manipur': (6347465, 5796243, 72359),
 'Mizoram': (1009532, 910925, 1770),
 'Goa': (13652667, 12301268, 188057),
 'Andaman and Nicobar Islands': (1283574, 1211641, 16203),
 'Assam': (56413792, 52528287, 260216),
 'Jharkhand': (31400674, 28349151, 296292),
 'Arunachal Pradesh': (3962184, 3686314, 12013),
 'Tripura': (8320110, 7728375, 92857),
 'Nagaland': (2846345, 2593039, 17581),
 'Meghalaya': (3074017, 2797072, 30784),
 'Dadra and Nagar Haveli': (186, 14, 0),
 'Sikkim': (1390467, 1243984, 26879),
 'unassigned': (280806, 0, 0),
 'Daman and Diu': (2, 0, 0),
 'Dadra and Nagar Haveli and Daman and Diu': (1022337, 940814, 662),
 'Lakshadweep': (107535, 73104, 164)}

df2 = pd.DataFrame(extracted2)
df2 = df2.transpose()
df2['state'] = df2.index
df2.columns = ['cases', 'cured', 'deaths', 'state']
df2.drop('unassigned', inplace=True)
df2

	cases	cured	deaths	state
Kerala	200185603	177894118	766581	Kerala
Delhi	158725093	147896560	2741459	Delhi
Telangana	75798728	69580382	432535	Telangana
Rajasthan	77767638	69179429	700476	Rajasthan
Haryana	66067411	60344316	698864	Haryana
Jammu and Kashmir	30756599	27720245	469131	Jammu and Kashmir
Karnataka	242380060	216792332	3144790	Karnataka
Ladakh	2307394	2084230	28077	Ladakh
Maharashtra	589598167	512544984	13590012	Maharashtra
Punjab	47764245	42219368	1406438	Punjab
Tamil Nadu	223058512	208216246	3261644	Tamil Nadu
Uttar Pradesh	160155913	143267909	2199456	Uttar Pradesh
Andhra Pradesh	228887306	215214582	1848944	Andhra Pradesh
Uttarakhand	23199910	20397560	370919	Uttarakhand
Odisha	82390133	77558828	426009	Odisha
West Bengal	133436340	123072064	2330712	West Bengal
Pondicherry	9996027	9076464	163604	Pondicherry
Chandigarh	5394798	4840252	77743	Chandigarh
Chhattisgarh	75304232	66298558	875894	Chhattisgarh
Gujarat	70290062	62010591	1330100	Gujarat
Himachal Pradesh	12567361	11065632	198464	Himachal Pradesh
Madhya Pradesh	66056188	59307071	976388	Madhya Pradesh
Bihar	69575592	64292363	381133	Bihar
Manipur	6347465	5796243	72359	Manipur
Mizoram	1009532	910925	1770	Mizoram
Goa	13652667	12301268	188057	Goa
Andaman and Nicobar Islands	1283574	1211641	16203	Andaman and Nicobar Islands
Assam	56413792	52528287	260216	Assam
Jharkhand	31400674	28349151	296292	Jharkhand
Arunachal Pradesh	3962184	3686314	12013	Arunachal Pradesh
Tripura	8320110	7728375	92857	Tripura
Nagaland	2846345	2593039	17581	Nagaland
Meghalaya	3074017	2797072	30784	Meghalaya
Dadra and Nagar Haveli	186	14	0	Dadra and Nagar Haveli
Sikkim	1390467	1243984	26879	Sikkim
Daman and Diu	2	0	0	Daman and Diu
Dadra and Nagar Haveli and Daman and Diu	1022337	940814	662	Dadra and Nagar Haveli and Daman and Diu
Lakshadweep	107535	73104	164	Lakshadweep

We can see there are some missing values (a lot actually!) that are labelled as 'unassigned.' For now we will drop these values from the table, although further analysis might be required to truly understand the effect of these values on the data overall.

# Make a chloropleth map!!

import folium

map = folium.Map(location=[22.71, 79.04], zoom_start=5)
map

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def color_map(column, scheme):
    map_osm = folium.Map(location=[22.71, 79.04], zoom_start=5)
    map_osm
    folium.Choropleth(
        geo_data="https://gist.githubusercontent.com/jbrobst/56c13bbbf9d97d187fea01ca62ea5112/raw/e388c4cae20aa53cb5090210a42ebb9b765c0a36/india_states.geojson",
        name="choropleth",
        data=df2,
        columns=["state", column],
        key_on='properties.ST_NM',
        fill_color=scheme,
        fill_opacity=0.7,
        line_opacity=0.2,
        legend_name=f"COVID-19 {column}",
    ).add_to(map_osm)

    folium.LayerControl().add_to(map_osm)
    return map_osm

color_map("deaths", "Reds")

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color_map("cured", "Greens")

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color_map("cases", "Purples")

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From these plots, we can seen that things seem pretty dire in Maharashta, the state where Mumbai is located. Maharashta has the greatest number of deaths, cases, and cured people. This makes a lot of sense because Mumbai, India's largest city, is located in Maharashta, and so there are some highly dense city areas there. In a city, especially a dense one, it is more difficult to socially distance, making it easier for COVID-19 to spread.

Looking at the other states, it appears that states in Southern India have it worse in number of cases, deaths, and cured people compared to Northern India. Kerala, which is in Southern India, is an interesting case study, because while it shares similar numbers in terms of cases and number of people cured, it has significantly fewer number of deaths than its neighboring states, such as Tamil Nadu and Karnataka.